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Aaron Schumacher
Instructor (Summer)

Aaron, a Metis data science instructor, develops systems that lead to knowledge and action.

Aaron Schumacher

Aaron, a Metis data science instructor, develops systems that lead to knowledge and action.

Aaron comes to Metis from consultancy Booz Allen Hamilton, where he helped government clients make effective use of data. His work has ranged from building visualization prototypes with the UK’s National Health Service and NYU’s GovLab to winning the Arlington Public Schools Big Data Roundtable by building predictive models for student outcomes. Aaron’s academic background is in math - after his BS at the University of Wisconsin-Madison, his MAT Mathematics thesis at Bard College demonstrated a novel construction of quaternion and octonion integers. Since then, Aaron has enjoyed working on more directly practical problems. Always passionate about education, Aaron first taught data science by that name in 2013; his students have gone on to work at companies including Airbnb, Infochimps, and Netflix.

Jonathan Hanke
Instructor (Summer - Fall)

Jonathan, a Metis data science instructor, loves using data to understand the world.

Jonathan Hanke

Jonathan, a Metis data science instructor, loves using data to understand the world.

Jonathan comes to Metis after working as an independent consultant with a background in Mathematics, Finance, and Software Development. His mathematical work on computational number theory and quadratic forms have focused on using computers to solve hard problems for over a decade, culminating with a proof of the 290-Theorem (jointly with 2014 Fields Medalist Manjul Bhargava). He has contributed extensively to the open-source SAGE computer algebra system and enjoys using the latest technologies to create a more productive world. Jonathan has been involved with mentoring at the PROMYS program for high school students at Boston University since he attended as a student 25 years ago, and currently serves as a Trustee of the PROMYS Foundation. He has also worked on Stochastic Portfolio Theory as a way of understanding the structure of equity markets through their price movements. Since receiving his Ph.D. in Mathematics from Princeton University, Jonathan has lectured internationally and taught mathematics at many interesting places including Princeton, Duke, Rutgers, and the University of Georgia over the last 15 years. His students have gone on to successful careers in academia, cryptography and data science.

Julia Lintern
Instructor (Fall - Winter)

Julia Lintern

Julia comes to Metis after working at JetBlue as a quantitative engineer. While at JetBlue, she used quantitative analysis and machine learning methods to provide continuous assessment of the aircraft fleet. Julia began her career as a structures engineer, where she designed repairs for damaged aircraft. In 2011, she transferred into a quantitative role at JetBlue and began her M.A. in Applied Math at Hunter College, where she focused on visualizations of various numerical methods including collocation and finite element methods. She discovered a deep appreciation for the combination of mathematics and visualizations and found data science to be a natural extension. Julia has also worked as an Expert in Residence for a company that provides data science training. She continues to collaborate on various projects including the development of stock trading algorithms. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.

Reshama Shaikh
Teaching Assistant

Reshama uses her statistics, business and programming skills to explore data and share the knowledge with other data science enthusiasts.

Reshama Shaikh

Reshama uses her statistics, business and programming skills to explore data and share the knowledge with other data science enthusiasts.

Reshama has over 10 years experience working in the pharmaceutical industry as a biostatistician. Reshama has her BA and MS in statistics (Rutgers University). She is also a francophile (Minor in French, Rutgers). She completed her MBA from NYU Stern in 2014 with a focus on business analytics and technology.

Megan Ayraud
Head of Careers

Megan, a technology recruiter, loves guiding people on their career paths and helping them find their next great jobs.

Megan Ayraud

Megan, a technology recruiter, loves guiding people on their career paths and helping them find their next great jobs.

Megan has been working as a recruiter in the tech community for the last 6 years. She is also the Co-Organizer at Women Who Code & a Zumba Instructor.

Leah Nicolai
Program Manager

Leah, a seasoned administrative professional, is passionate about connecting to people and helping them achieve their educational goals.

Leah Nicolai

Leah, a seasoned administrative professional, is passionate about connecting to people and helping them achieve their educational goals.

Before becoming Program Manager at Metis, Leah worked for (and currently still does) Kaplan Test Prep, along with working in the admissions department at Metis. Leah graduated with a degree in Art History and Spanish and prior to joining Kaplan, worked at an investment bank, managing all aspects of the company and its employees. She also teaches art with Free Arts NYC and at the Harlem YMCA. In her spare time, she enjoys any craft-based project, traveling, hiking and Jeopardy.

The Details

The Pre-Requisites

Applicants must have some previous experience programming (writing code) and studying or using statistics.

The Outcome

Upon graduating, you will be comfortable designing, implementing, and communicating the results of a
data science project, including knowing the fundamentals of data visualization and having introductory
exposure to modern big data tools and architecture such as the Hadoop stack. You should feel confident
pursuing a job as an entry-level data scientist or data analyst. Read our syllabus

Total Cost: $14,000 for 12 weeks

We offer a $2,000 scholarship for women,
underrepresented minority groups, and veterans or members of the U.S. military.

$2,000 Scholarship

If you are a woman, part of a underrepresented minority group*, or a veteran or
member of the U.S. military, you will be automatically eligible to receive a $2,000
scholarship toward your Metis tuition.

Why? The story is a familiar and unfortunate one:

Women make up less than one-third of all employees in the tech sector. Tech
companies employ an average of 12.33 percent female engineers. Women contribute
to just 1.2 percent of open source software.1

Only four percent of people in software development, application and systems
jobs are African-American and five percent are Hispanic or Latino.2

Women of color represent less than three percent of the people in technology
fields.3

As a country, we need to reverse these trends and create more avenues for talented
individuals from underrepresented demographic groups and communities to help
drive our future economic growth. This scholarship is a step toward supporting a
more diverse workforce. Scholarship funds are applied to Metis tuition only and are
not transferable.

Scholarship eligibility is subject to validation. Metis has sole discretion in the award
of the Scholarship and the right to revoke the Scholarship offer for prospective
applicants at any time.

Bootcamp Structure & Syllabus

The bootcamp experience is intense, but we aim to maximize learning while preventing
burn-out. Metis believes that a student’s brain is like a muscle, and to grow without injury
the brain must take time to recover. Each Monday - Friday consists of, on average, three hours of group classroom instruction and five hours of practical skill development and project work.

Online Pre-Work

We’ll provide a Command Line Crash Course, tutorials to become familiar with Python,
and a number of package installation tutorials (i.e., numpy, scipy, pandas, scikit.learn),
as well as some preliminary statistics work. Test-out/check-out modules will let students
know when they are “prepared enough” for class.

After the at-home pre-work phase, we will convene in class and spend our first 8 weeks
together doing iterative, project-centered skill-acquisition. Over the course of four data
science projects we’ll "train up" different key aspects of data science, and results from
each project will be added to the students' portfolios. In the last four weeks, students
build out and complete their individual Passion Projects, culminating in a Career Day
reveal of their work to representatives from our Metis Metis Hiring Network.

WEEK 1

UNIT ONE Introduction to the Data Science Toolkit

Students complete an entire bite-sized data science project from start to finish. They start using Git for version control and the IPython environment with the pandas and matplotlib packages to perform exploratory statistical analyses and visualizations.

In the first week, students work in small groups using MTA turnstile data to estimate the volume of people on the street, so that (theoretical) nonprofits and companies can deploy street teams efficiently. The students are provided with the data and guided through exploratory data analysis and plotting so they can focus on new tools, brainstorming, and communication.

WEEK 2

UNIT TWO: PART 1Design Process and Web Scraping

In preparation for Project 2, students start to learn one of the most important tools a data scientist uses: the iterative design process. They learn tools for web scraping and start fitting simple models to data. Also, they are introduced to cloud computing and work on remote servers.

Use the design process to iteratively explore the possible ways that a problem can be solved

Create and work in a virtual environment on a cloud computing service

Use Python’s Requests and Selenium packages to obtain data from web pages

Use Python’s Beautiful Soup package to parse the content of a web page to find useful data for subsequent analysis

Use the design process to iterate the concept for the Unit 2 projects

Complete a primer on web fundamentals including HTML, CSS, and JavaScript

WEEK 3

UNIT TWO: PART 2 Regression, Communicating Results

Students go in-depth on regression using scikit-learn and matplotlib. Choosing among the analysis methods and approaches to reporting their results, students finish the second project and present their findings.

For the first pass at machine learning, students dive deep into prediction with regression models. They experience the beauty of flat files, and learn to scrape information from web sites using tools like Python Requests, Beautiful Soup, and Selenium. After scraping together some movie box office data, students find and scrape more resources on their own and present their movie industry regression predictions to the class.

WEEK 4

Students cover relational databases such as SQL and more ways of obtaining, cleaning and maintaining data. They are introduced to the concepts of machine learning and exposed to classification and supervised learning with a few examples such as logistic regression and KNN. They also discuss different types of feasibility related to data science questions and projects.

Week 5

Students dig into more details and more algorithms for supervised learning including SVM, decision trees and random forests; techniques for feature selection and feature extraction; and concepts and applications for deep learning. Students choose to apply one or more of these algorithms as part of this Unit’s project.

Evaluate the efficacy and computational feasibility of various ML algorithms in different contexts

Week 6

UNIT THREE: PART 3JavaScript and D3.js

Students visualize projects using D3, a favorite tool for flexible and attractive presentations of data and relationships. Since D3 is a JavaScript library, students learn JavaScript essentials and the incorporation of other js libraries (jQuery, Bootstrap, etc.) that make the job much easier.

Students form small groups that each work as an internal data science team at a fictional company in the insurance industry (details are left to the students to determine). Supervised learning algorithms and relational databases have been covered in class. Students work on their own classification models that fit within the overall goals of the company and the team. During McNulty, students perform a deep dive into the visualization package D3 and create their own APIs on the Python Flask micro framework to serve data from their databases to their visualizations.

Week 7

The project for the fourth unit involves text data. Students round out data acquisition methods with APIs and online database servers. Students also learn about NoSQL databases and start using MongoDB.

Week 8

UNIT FOUR: PART 2Natural Language Processing (NLP)

Students analyze the text data collected in the previous week and learn about NLP algorithms. More unsupervised learning algorithms are explored. Students dive deeper into unsupervised learning and more algorithms, covering K-means, hierarchical clustering, mixture models and topic models. They also learn about how large amounts of data are handled, discussing parallel computing and Hadoop MapReduce. Project 4 presentations are presented as lightning talks.

The last guided project focuses on unsupervised learning and NLP algorithms, NoSQL databases, and API data collection. Students work individually and have very few constraints for the design of this project.

Weeks 9-12

UNIT FIVEFinal Project

Students work full time on their Final Projects, which they have been slowly designing through the first eight weeks. They also learn more about cloud computing, system architectures and feasibility evaluations.

Design and develop a data visualization to clearly convey the results of the analysis to a layperson

Assemble final portfolio and present project at Career Day

PROJECT 5: CODENAME KOJAK

Students are free to use anything covered in class or to learn something new to answer the questions they want to address. Some students know what will be their final project at the admissions stage. Others embark on entirely new turf. Every student works intensely and challenges him or herself to create something cool, interesting, useful, or worthwhile.

Objectives

Upon graduating from the Data Science bootcamp, a student will be prepared to take
an entry-level position on a Data Science team as a data scientist or data analyst. This
means a student will:

Have a fluid understanding of and practical experience with the process of designing,
implementing, and communicating the results of a data science project.

Be capable coders in Python and at the command line, including the related packages
and toolsets most commonly used in data science.

Understand the landscape of data science tools and their applications, and be prepared
to identify and dig into new technologies and algorithms needed for the job at hand.

Know the fundamentals of data visualization and have experience creating static and
dynamic data visuals using JavaScript and D3.js.

Have introductory exposure to modern big data tools and architecture such as the
Hadoop stack, know when these tools are necessary, and be poised to quickly train up
and utilize them in a big data project.

The Bootcamp Experience

Instructor and curriculum co-developer Irmak Sirer explores the qualities of a great data scientist, who should apply to the Metis Data Science Bootcamp, and more.

*Applying early better ensures that you will have a seat in the next cohort if you are accepted. Because seats are limited, accepted students that wait until the final application deadline may be invited to join the following cohort.

Metis, a d/b/a of Kaplan Test Prep, is accredited by the Accrediting Council for Continuing Education & Training (ACCET), a U.S. Department of Education nationally recognized agency.